class_docstr: Proposes new trials using a linear model.
  
  The model suggests an architecture no deeper than those in the trials.
  Sometimes (by a coin flip) adds one layer of depth to the suggestion.
class_docstr_tok: ["Proposes","new","trials","using","a","linear","model",".","The","model","suggests","an","architecture","no","deeper","than","those","in","the","trials",".","Sometimes","(","by","a","coin","flip",")","adds","one","layer","of","depth","to","the","suggestion","."]
docstr: Load trial architectures from filesystem.
docstr_tok: ["Load","trial","architectures","from","filesystem","."]
code: def _load_trials(self, trials):
      """Load trial architectures from filesystem."""
  
      completed_trials = trials
  
      architectures = []
      losses = []
  
      for trial in completed_trials:
          directory = architecture_utils.DirectoryHandler.trial_dir(
              trial
          )
          architecture = architecture_utils.get_architecture(directory)
          # The location of the flatten block is fixed
          # by the transition from convolutional to fully-connected layers.
          # It should not be a part of our search problem.
          # It will be placed by architecture_utils.fix_architecture_order().
          filtered = np.array(
              [
                  block
                  for block in architecture
                  if block not in block_builder.FLATTEN_TYPES
              ]
          )
          architectures.append(filtered)
          losses.append(trial.final_measurement.objective_value)
  
      return architectures, np.array(losses)
  
file_path: model_search/model_search/search/linear_model.py
imports: from model_search import block_builder
  from model_search.architecture import architecture_utils
  from model_search.proto import phoenix_spec_pb2
  from model_search.search import common
  from model_search.search import search_algorithm
  import numpy
  import sklearn
  import sklearn.linear_model
label_desc: Use image node_id 5 for calling the LinearModel obj's underlying member method code with example usage: obj._load_trials(trials) and returns: architectures,  np